An entropy minimization principle for semi-supervised terrain classification

Analyzing the structure of the family of cost functions that are minimum when the classifier outputs are equal to the class probabilities, we found that all of them can be expressed as sum of a generalized entropy measure and an error component. This suggests a novel algorithm for classification that uses both labeled an unlabeled data and is based on the following idea: use labeled data to minimize the cost function and unlabeled data to minimize the corresponding entropy measure. This entropy minimization principle is applied to terrain classification of Landsat images.